Geochemical potential mapping of iron-oxide targets by Prediction-Area plot and Concentration-Number fractal model in Esfordi, Iran

Document Type : Research Paper

Authors

1 Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

2 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

This study serves the purpose of generating a geochemical Fe-bearing potential map. Stream sediment geochemical survey was employed by collecting 843 samples for analyzing 19 elements and oxides. Taking preprocessing of data (e.g. outlier correction and data normalization) into consideration, a Concentration–Number (C-N) fractal model was used to separate different geochemical populations of Fe2O3, TiO2, V and the main multi-element factor in close spatially association with the Fe targeting. A prediction-area (P-A) plot was drawn for each variable to determine the weight of each geochemical indicator. Results indicate that the main geochemical factor with an ore prediction rate of 73%, has occupied 27% of the Esfordi area as favorable zones for further mining propsectivity. The Esfordi as a favorable Fe-bearing zone is of special interest in the NE of the Bafq mining district that hosts important “Kiruna-type” Magnetite-Apatite deposits. In addition, a synthesized indicators map was prepared through implementing a data-driven multi-class index overlay in a similar fashion to the previous version of the method, upon which geochemical potential zones were mostly in the NE part of the Esfordi, intimately linked with intense fault density map. The significance of this study lies in localizing of the most geochemical favorable zones through simultaneous consideration of the C-N and P-A plots accompanied with the incorporation of known active mines and prospects to determine indicator weight. Of note is that the Mineral Potential Mapping (MPM) has higher efficiency over each geochemical indicator with an ore prediction rate of 78% and area occupation of 22%.

Keywords


[1] Karimpour M. 1989. Applied Economic Geology. Javid Publication, Mashhad, Iran, 404 pp.
[2] Förster, H., and Jafarzadeh, A., (1994). The Bafq mining district in Central Iran - a highly mineralized Infracambrian volcanic field:
[3] Mazaheri S. A., Andrew A. S. &Chenhall B. E. (1994). Petrological studies of Sangan iron ore deposit. Center for isotope studies, Research Report, Sydney, Australia, pp. 48-52.
[4] Daliran F (2002) Kiruna-type iron oxide-apatite ores and apatitites of the Bafq district, Iran, with an emphasis on the REE geochemistry of their apatites. In: Porter, T.M. (Ed.), Hydrothermal Iron Oxide Copper-Gold and Related Deposits: A Global Perspective, vol. 2. PGC Publishing, Adelaide, pp. 303–320.
[5] Maanijou M. (2002). Proterozoic metallogeny of Iran. In: International Symposium of Metallogeny of Precambrian Shields, p. 2.13. Kyiv, Ukraine.
[6] Daliran F., Stosch H. G. & Williams P. (2007). Multistage metasomatism and mineralization at hydrothermal Fe oxide REE-apatite deposits and ‘apatitites’ of the Bafq district, centraleast Iran. In: Stanely C. J. eds. Digging Deeper, pp. 1501-1504. Proceedings 9th Biennial SGA Meeting Dublin, Ireland.
[7] Daliran F, Stosch HG, Williams P, Jamali H, Dorri MB (2010) Early Cambrian iron oxide-apatite-REE (U) deposits of the Bafq District, east-central Iran. Exploring for Iron oxide copper-gold deposits: Canada and Global analogues. Geol Assoc Canada, Short Course Notes 20: 143- 155.
[8] Jami M., Dunlop A. C. & Cohen D. R. (2007). Fluid inclusion and stable isotope study of the Esfordi apatite-magnetite deposit, Central Iran. Economic Geology 102, 1111-1128.
[9] Daliran, F., (1990). The magnetite-apatite deposit of Mishdovan, East Central Iran. An alkali rhyolite hosted, “Kiruna type” occurrence in the InfracambrianBafqmetallotect (mineralogic, petrographic and geochemical study of the ores and the host rocks): Ph.D. thesis, Heidelberg, Heidelberger GeowissenschaftlicheAbhandlungen 37, 248 p.
[10] Samani B. (1993). Saghand Formation, a riftogenic unit of upper Precambrian in Central Iran. Geosciences Scientific Quarterly Journal 6, 32-45. (In Persian with English abstract).
[11] Bonyadi Z., Davidson G. J.,Mehrabi B.,Meffre S. &Ghazban F. (2011). Significance of apatite REE depletion and monazite inclusions in the brecciated Se-Chahun iron oxide-apatite deposit, Bafq district, Iran: Insights from paragenesis and geochemistry. Chemical Geology 281, 253-269.
[12] Torab F., M. Lehmann B. (2006). Iron oxide-apatite deposits of the Bafq district, Central Iran: an overview from geology to mining. World of Mining—Surface and Underground 58, 355-362.
[13] Mohammad Torab, F., (2008). Geochemistry and metallogeny of magnetite apatite deposits of the Bafq Mining District, Central Iran, Doctoral Thesis, Faculty of Energy and the Economic Sciences Clausthal University of Technology.
[14] Sadeghi, B., Khalajmasoumi, M., Afzal, P., Moarefvand, P., Yasrebi, A. B., Wetherelt, A., &Ziazarifi, A. (2013). Using ETM+ and ASTER sensors to identify iron occurrences in the Esfordi 1: 100,000 mapping sheet of Central Iran. Journal of African Earth Sciences, 85, 103-114.
[15] Samani, B.A., (1988).Metallogeny of the Precambrian in Iran: Precambrian Research, v. 39, p. 85-106.
[16] Mücke, A., Younessi, R., (1994). Magnetite-apatite deposits (Kiruna-type) along the Sanandaj-Sirjan zone and in the Bafq area, Iran, associated with ultramafic and calc-alkaline rocks and carbonatites: Mineralogy and Petrology, v. 50, p. 219-244.
[17] Harris J, Wilkinson L, Grunsky E, Heather K, Ayer J (1999). Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario, Journal of Geochemical Exploration 67:301-334.
[18] Cheng Q (1999). Spatial and scaling modelling for geochemical anomaly separation, Journal of Geochemical exploration 65:175-194.
[19] Cheng, Q., Agterberg, F. and Bonham-Carter, G., (1996) A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration, 56: 183-195.
[20] Ghavami-Riabi, R., Seyedrahimi-Niaraq, M., Khalokakaie, R. and Hazareh, M., (2010) U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical Exploration, 104: 27-33.
[21] Darabi-Golestan, F., Ghavami-Riabi, R., Khalokakaie, R., Asadi-Haroni, H., Seyedrahimi-Niaraq, M., 2013, Interpretation of lithogeochemical and geophysical data to identify the buried mineralized area in Cu-Au porphyry of Dalli-Northern Hill. Arabian Journal of Geosciences, 6:4499-4509.
[22] Seyedrahimi-Niaraq, M., Hekmatnejad, A., (2020). The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district, NW Iran. ActaGeochimica, https://doi.org/10.1007/s11631-020-004137.
[23] Qiuming C (2000).Multifractal theory and geochemical element distribution pattern, Earth Science-Journal of China University of Geosciences 25:311-318.
[24] Hawkes RAW, Webb HE (1979). Geochemistry in mineral exploration,2nd edn. Academic Press, New York, 657 pp.
[25] Li, C.J., Ma, T.H., Shi, J.F., (2003). Application of a fractal method relating concentration and distances for separation of geochemical anomalies from background. J GeochemExplor 77: 167–175.
[26] Mandelbrot, B.B., (1983). The Fractal Geometry of Nature. WH Freeman, San Francisco, pp 1-468.
[27] Cheng, Q., Agterberg, F.P., Ballantyne, S.B., (1994). The separation of geochemical anomalies from background by fractal methods. J GeochemExplor 51: 109–130.
[28] Afzal, P., FadakarAlghalandis, Y., Khakzad, A., Moarefvand, P., RashidnejadOmran, N., (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J GeochemExplor 108: 220–232.
[29] Afzal, P., DadashzadehAhari, H., RashidnejadOmran, N., Aliyari, F., (2013). Delineation of gold mineralized zones using concentration-volume fractal model in Qolqoleh gold deposit, NW Iran. Ore Geology Reviews 55: 125-133.
[30] Delavar, S.T., Afzal, P., Borg, G., Rasa, I., Lotfi, M., RashidnejadOmran, N., (2012). Delineation of mineralization zones using concentration-volume fractal method in Pb-Zn carbonate hosted deposits. J. Geochem. Explor. 118, 98–110.
[31] Zuo, R., (2011). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Appl. Geochem. 26, S271–S273.
[32] Zuo, R., Wang, J., (2016). Fractal/multifractal modeling of geochemical data: a review. J. Geochem. Explor. 164, 33–41.
[33] Panahi, A., Cheng, Q., & Bonham-Carter, G. F. (2004). Modelling component, indicator kriging, and multifractal power-spectrum analysis: a case study from Gowganda, Ontario. Geochemistry: Exploration, Environment, Analysis, 4(1), 59-70.
[34] Mirzaie, M., Afzal, P., Adib, A., Rahimi, E., &Mohammadi, G. (2020). Detection of zones based on ore and gangue using fractal and multivariate analysis in ChahGaz iron ore deposit, Central Iran. Journal of Mining and Environment, 11(2), 453- 466.
[35] Nyka¨nen, V., Lahti, I., Niiranen, T., &Korhonen, K. (2015). Receiver operating characteristics (ROC) as validation tool for prospectivity models—a magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland. Ore Geology Reviews, 71, 853–860.
[36] Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers &Geosciences, 79, 69-81.
[37] Carranza, E. J. M., &Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research, 25, 35–50.
[38] Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 89, 171–183.nces, 85, 103-114.
[39] Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25, 3–18.
[40] Du, X., Zhou, K., Cui, Y., Wang, J., Zhang, N., & Sun, W. (2016). Application of fuzzy Analytical Hierarchy Process (AHP) and Prediction-Area (P-A) plot for mineral prospectivity mapping: A case study from the Dananhumetallogenic belt, Xinjiang, NW China. Arabian Journal of Geosciences, 9, 298.
[41] Gao, Y., Zhang, Z., Xiong, Y., &Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews, 75: 16–28.
[42] Nezhad, S. G., Mokhtari, A. R., &Rodsari, P. R. (2017). The true sample catchment basin approach in the analysis of stream sediment geochemical data. Ore Geology Reviews, 83, 127–134.
[43] Zhang, N., Zhou, K., & Du, X. (2017). Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China. Journal of African Earth Sciences, 128, 84–96.
[44] Almasi, A., Yousefi, M., & Carranza, E. J. M. (2017). Prospectivityanalysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran. Ore Geology Reviews, 91, 1066–1080.
[45] Roshanravan, B., Aghajani, H., Yousefi, M., &Kreuzer, O. (2019). An improved prediction-area plot for prospectivity analysis of mineral deposits. Natural Resources Research, 28(3), 1089-1105.
[46] Bishop, C. M. (2006). Pattern recognition and machine learning. springer. [47] Coolbaugh, M.F., Raines, G.L., Zehner, R.E., (2007).Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources. Nat. Resourc.Res.16, 199–207.
[48] Gansser A. (1981). The Geodynamic History of the Himalaya, in Zagros, Hindu Kush. In: Gupta H. K. & Delany F. M. eds. Himalaya-Geodynamik Evolution, Geodynamik Series, 3, pp. 111-121. American Geophysical Union, Washington DC. [49] Stocklin J. (1971). Stratigraphic Lexicon of Iran; Part 1: Tehran, Geological Survey of Iran, 338 p. Tehran Iran.
[50] Alavi M. (1991). Sedimentary and structural characteristics of the Paleo-Tethys remnants in northeastern Iran. Geological Society of America Bulletin 103, 983-992.
[51] Daliran F, Stosch HG, Williams PJ (2009). A review of the Early Cambrian magmatic and metasomatic events and their bearing on the genesis of the Fe oxide-REE-apatite deposits (IOA) of the Bafq district, Iran. In: Williams P (Ed.): Smart Science for Exploration and Mining. 10th SGA Biennial, Townsville, 623– 625.
[52] Gandhi SS (2003). An overview of the Fe oxide–Cu–Au deposits and related deposit types. CIM Montréal 2003 Mining Industry Conference and Exhibition, Canadian Institute of Mining, Technical Paper, CD-ROM.
[53] Williams PJ (2010). Classifying IOCG deposits. In: Corriveau L, Mumin H (Eds) Exploring for iron-oxide copper-gold deposits: Canada and Global Analogues: Geological Association. Canada short course notes 20: 11–19.
[54] Groves DI, Bierlein FP, Meinert LD, Hitzman MW (2010). Iron oxide copper-gold (IOCG) deposits through earth history: implications for origin, lithospheric setting, and distinction from other epigenetic iron oxide deposits. Economic Geology 105: 641-654.
[55] Ghorbani, M. (2013). Economic geology of Iran (Vol. 581). Berlin: Springer.
[56] Stosch HG, Romer RL, Daliran F, Rhede D (2011). Uranium-lead ages of apatite from iron oxide ores of the Bafq District, East-Central Iran. Miner Deposita46: 9–21.
[57] Nisco-National Iranian Steel Company (1980). Report on results of search and evaluation works at magnetic anomalies of the Bafq iron ore region during 1976-1979. Unpublished Report, p 260.
[58] Berberian M., & King G. C. P. (1981). Towards the Paleogeography and tectonic evolution of Iran. Canadian Journal of Earth Sciences 18, 210-265.
[59] Nabatian, G., Rastad, E., Neubauer, F., Honarmand, M., &Ghaderi, M. (2015). Iron and Fe–Mnmineralisation in Iran: implications for Tethyanmetallogeny. Australian Journal of Earth Sciences, 62(2), 211-241.
[60] Olea, R. A. (2006). A six-step practical approach to semivariogram modeling. Stochastic Environmental Research and Risk Assessment, 20(5), 307-318.
[61] Tahernejad, M. M., KhaloKakaei, R., &Ataei, M. (2018). Analyzing the effect of ore grade uncertainty in open pit mine planning; A case study of Rezvan iron mine, Iran. International Journal of Mining and Geo-Engineering, 52(1), 53-60.
[62] Riemann, C., Filzmoser, P., & Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 17(3), 185-206.
[63] Nazarpour, A., Omran, N. R., &Paydar, G. R. (2015). Application of multifractal models to identify geochemical anomalies in Zarshuran Au deposit, NW Iran. Arabian Journal of Geosciences, 8(2), 877-889.
[64] Khalifani, F., Bahroudi, A., Barak, S., &Abedi, M. (2019). An integrated Fuzzy AHP-VIKOR method for gold potential mapping in Saqez prospecting zone, Iran. Earth Observation and Geomatics Engineering, 3(1), 21-33.
[65] Grant, A. (1990). Multivariate statistical analyses of sediment geochemistry. Marine Pollution Bulletin, 21(6), 297-299. [66] Zumlot, A. B. T. (2012). Multivariate statistical approach to geochemical methods in water quality factor identification; application to the shallow aquifer system of the Yarmouk Basin of north Jordan. Research Journal of Environmental and Earth Sciences, 4(7), 756-768.
[67] Ammar, F. H., Chkir, N., Zouari, K., Hamelin, B., Deschamps, P., &Aigoun, A. (2014). Hydro-geochemical processes in the Complexe Terminal aquifer of southern Tunisia: An integrated investigation based on geochemical and multivariate statistical methods. Journal of African Earth Sciences, 100, 81-95.
[68] Karar, K., Gupta, A. K., Kumar, A., & Biswas, A. K. (2006). Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese, and iron in PM 10 particulates at the two sites of Kolkata, India. Environmental Monitoring and Assessment, 120(1-3), 347-360.
[69] Sprovieri, R., Thunell, R., & Howe, M. (2020). Paleontological and geochemical analysis of three laminated sedimentary units of late Pliocene-early Pleistocene age from the Monte San Nicola section in Sicily. RivistaItaliana di Paleontologia e Stratigrafia, 92(3).
[70] Hirst, D. M. (1974). Geochemistry of Sediments from Eleven Black Sea Cores: Geochemistry.
[71] Malinowski, E. R., &Howery, D. G. (1980). Factor analysis in chemistry (p. 10). New York: Wiley.
[72] Wu, R., Chen, J., Zhao, J., Chen, J., & Chen, S. (2020). Identifying Geochemical Anomalies Associated with Gold Mineralization Using Factor Analysis and Spectrum–Area Multifractal Model in Laowan District, Qinling-DabieMetallogenic Belt, Central China. Minerals, 10(3), 229.
[73] Goncalves MA, Vairinho M, Oliveira V (1998). Study of geochemical anomalies in Mombeja area using a multifractal methodology and geostatistics. In: Buccianti A, Nardi G, Potenza R (eds) IV IAMG'98. De Frede, Ischia Island, Italy, pp 590–595.
[74] Afzal, P., Zia Zarifi, A., &Sadeghi, B. (2013). Separation of Geochemical Anomalies Using Factor Analysis and Concentration-Number (CN) Fractal Modeling Based on Stream Sediments Data in Esfordi 1: 100000 Sheet, Central Iran. Iranian Journal of Earth Sciences, 5(2), 100-110.
[75] Momeni, S., Shahrokhi, S. V., Afzal, P., Sadeghi, B., Farhadinejad, T., &Nikzad, M. R. (2016). Delineation of the Cr mineralization based on the stream sediment data utilizing fractal modeling and factor analysis in the Khoy 1: 100,000 sheet, NW Iran. MadenTetkikveAramaDergisi, (152), 143-151.
[76] Asfahani, J. (2017). Fractal theory modeling for interpreting nuclear and electrical well logging data and establishing lithological cross-section in basaltic environment (case study from southern Syria). Applied Radiation and Isotopes, 123, 26-